Conventional colonoscopy has been used for screening of colorectal cancer. However, as the colon is a tortuous and pliable tube with many internal ridges and sharp bends, advancement of the colonoscope is difficult and often complicated by excessive looping and stretching of the colon. This may cause significant pain and discomfort as well as a substantial risk of over-distension or even perforation of the colon. A high degree of technical expertise from the medical practitioner is therefore required for skillful manipulation of the colonoscope.
Robotics technology has advantages that can be incorporated into endoscopes for a variety of applications, including colonoscopy. For example, by exploiting soft deformable structures that are capable of moving effectively through a complex environment like the inside the colon, one can significantly reduce pain and patient discomfort while minimizing the risk of colonic perforation. However, the guidance of such robotic endoscopes may still require significant technical expertise by the medical practitioner.
Accordingly, a need exists for improved systems and methods for automated steering control of endoscopes in general, and in particular, for the steering of robotic colonoscopes. A variety of image processing-based approaches to identifying the center of the colon lumen and determining a navigation direction for an advancing colonoscope have been described in the literature (see, for example, Reilink, et al. (2010), “Image-Based Flexible Endoscope Steering”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 18-22, 2010, Taipei, Taiwan, page 2339; van der Stap, et al. (2012), “The Use of the Focus of Expansion for Automated Steering of Flexible Endoscopes”, The Fourth IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Roma, Italy. Jun. 24-27, 2012, page 13; Ciuti, et al. (2012), “Intra-Operative Monocular 3D Reconstruction for Image-Guided Navigation in Active Locomotion Capsule Endoscopy”, IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 768-774; Bell, et al. (2013), “Image Partitioning and Illumination in Image Based Pose Detection for Teloperated Flexible Endoscopes”, Artificial Intelligence in Medicine 59(3):185-196; van der Stap, et al. (2013), “Towards Automated Visual Flexible Endoscope Navigation”, Surg Endosc. 27(10):3539-3547; Pullens, et al. (2016), “Colonoscopy with Robotic Steering and Automated Lumen Centralization: a Feasibility Study in a Colon Model”, Endoscopy 48(3):286-290; and van der Stap, et al. (2016), “Feasibility of Automated Target Centralization in Colonoscopy”, Int J Comput Assist Radiol Surg. 11(3):457-465). To date however, no single approach has been sufficiently reliable to augment or replace the know-how provided by a skilled surgeon. The present disclosure describes novel methods and systems that utilize data provided by a variety of image sensors and/or non-imaging sensors, image processing using a combination of image processing algorithms, and/or machine learning algorithms to identify the center position of the colon lumen ahead of an advancing colonoscope and determine a navigation direction. The use of a machine learning approach allows the system to simulate the surgeon's thought process in guiding the progress of the colonoscope. The disclosed methods and systems are applicable to a variety of other endoscope applications in addition to colonoscopy.